Efficient path planning and data gathering protocols for the wireless sensor network

The advantages of deploying mobile sinks (MSs) in wireless sensor networks (WSNs) have attracted tremendous attention recently, because mobility of a sink provides an efficient method to improve data gathering performance in WSNs. However, in such kind of WSN, the efficiency of data dissemination is obviously affected by the trajectory of the MS. Therefore, optimal moving schemes of the MS have been studied in many researches. Nevertheless, in many cases, the trajectory of the MS is not able to change ideally as previous studies assumed but depending on the environmental conditions. In this paper, we focus on the efficiency of data gathering in WSN with a MS whose trajectory cannot change ideally and propose a new data diffusion scheme - implementing a new Pre-Diffusion Algorithm (PDA) in a hierarchical WSN aimed at improving data gathering efficiency. This hierarchical WSN is composed of several clusters, and datatransmission is done by higher powered cluster head (CH) node. Our scheme predetermines the cluster area where the MS is going to arrive in most probability in next period of time. MS fulfills data converging in the cluster head (CH)whose distance to the MS may the smallest compared with other CHs when data gathering starts. Thus more data can be collected with less trajectory length of MS. Simulation results show that our proposed scheme improves data gathering efficiency as compared to conventional scheme.

Sink mobility is one of the efficient solutions to sink hole or energy hole problem which is usually caused by multi-hop communication using a static sink. However, the design of an efficient path for the mobile sink is an important issue. In this paper, we propose a novel algorithms to determine rendezvous point (RP) based dynamic path for mobile sink called delay aware energy density based trajectory (DAEDT). The DAEDT designs an energy efficient delay bound path for the mobile sink. In order to balance energy consumption among the sensor nodes, the proposed algorithm selects RPs based on the energy density of the sensor nodes. The DAEDT is also presented with a detour criteria following some threshold. The algorithm is extensively simulated and the results of DAEDT are compared with some existing schemes to show its effectiveness over various performance metrics. The simulation results are further validated through hypothesis testing using ANOVA.

Recent years have witnessed a surge of the interest in efficient data gathering schemes in wireless sensor networks (WSNs). In this paper, we address this essential issue in WSNs by introducing the mobility and MIMO capability to the data sink in order to optimize the system performance. Specifically, the mobile sink moves along a pre-determined path and collects data packets at some polling stations. With a proper planning on the moving path, we can reduce the data relaying to the minimum, which effectively improves the energy efficiency and also circumvents the non-uniformity of energy consumption among sensor nodes. We also apply MIMO technique to the mobile sink by mounting multiple antennas on it, which provides a radical way to improve the system capacity. With the support of MIMO technique, the mobile sink can simultaneously collect data from multiple compatible sensors. Finally, we theoretically analyze the performance of mobile data gathering in a WSN enhanced by MIMO support, and also investigate its efficiency by simulation experiments. The results demonstrate that the proposed mechanism can greatly improve the system throughput and energy efficiency with minimum additional overhead.

In wireless sensor networks (WSNs), sensor nodes near static sink will have more traffic load to forward and the network lifetime will get largely reduced. This problem is referred to as the hotspot problem. Recently, adopting sink mobility has been considered as a good strategy to overcome the hotspot problem. Despite its many advantages, due to the dynamic network topology caused by sink mobility, data transmission to the mobile sink is a challenging task. To achieve efficient data dissemination, nodes need to reconstruct their routes toward the latest location of the mobile sink, which weakens the energy conservation aim. In this paper, we proposed an energy-efficient cluster-based dynamic routes adjustment approach (EECDRA) which aims to minimize the routes reconstruction cost of the sensor nodes while maintaining nearly optimal routes to the latest location of the mobile sinks. The network is divided into several equal clusters and cluster heads are selected within each cluster. We also set some communication rules that manage routes reconstruction process accordingly requiring only a limited number of nodes to readjust their data delivery routes toward the mobile sinks. Simulation results show that the mobile sinks for reducing reconstruction of route have improved the energy efficiency and prolonged lifetime of wireless sensor network.

This study discusses about various approaches available for achieving energy-efficient data collection in wireless sensor networks. In wireless sensor networks, movable sink (MULE-Mobile Ubiquitous LAN Extensions) moving at a constant pace over a constrained path may increase energy efficiency. The mobile sink may randomly visit each sensor node and gather data in a short amount of time. These methods provide several hurdles in terms of data collection and power usage. To overcome this issue, this study has created the Maximum Amount Shortest Path (MASP) data gathering approach, which boosts performance. The MASP system is implemented using a twophase communication mechanism. Simulations in NS2 are used to test the suggested algorithms and protocols. The wireless sensor network has gotten a lot of interest in the academic community in the past several years. Many factors, such as resource limits on energy and power needed for computing, have been extensively researched. Because sensor nodes are battery-operated devices, the fundamental challenge in Wireless Sensor Networks i s reducing the amount of energy spent by the nodes so that the network’s lifetime may be extended. WSN also has privacy problems. Taking these two factors into consideration, this study examines the energy consumption of sensor node components and highlight the key possibilities for energy conservation in WSN in this work. Different battery management and security strategies that are currently available in WSN are also analyzed. Finally, this research study has come up with some energy saving ideas for future WSN research.

Wireless sensor networks (WSNs) are considered the backbone of the Internet of Things (IoT), which enables sensor nodes (SNs) to achieve applications similarly to human intelligence. However, integrating a WSN with the IoT is challenging and causes issues that require careful exploration. Prolonging the lifetime of a network through appropriately utilising energy consumption is among the essential challenges due to the limited resources of SNs. Thus, recent research has examined mobile sinks (MSs), which have been introduced to improve the overall efficiency of WSNs. MSs bear the burden of data collection instead of consuming energy at the routeing by SNs. In a network, some areas generate more data through SNs that contain frequent, urgent messages. These messages carry sensitive data that must be delivered immediately to user applications. Collecting such messages via MSs, especially on a large scale, increases delays, which are not tolerable in some real applications. This issue has not been studied much. Thus, the present study utilises the advantages of the priority parameter to concentrate on these areas and proposes a new model named ‘energy efficient path planning of MS-based area priority’ (EEPP-BAP). This method involves non-urgent and urgent messages. It is comprised of four procedures. Initially, after SNs are distributed randomly in a wide monitoring field, the monitoring field is partitioned into equal zones according to priority, either differently or equally. Next is clustering based on the cluster head (CH) selected to perform the particle swarm optimisation algorithm (PSO). Then, the MS moves first to the zones with higher priority and less distance to perform the brain storm optimisation algorithm. Finally, for urgent messages from the other zones at which the MS continues, the proposed approach establishes a routeing technique using multi-hop communication based on the MS position and using PSO. The proposed solution is aimed at delivering urgent messages to MSs free of latency and with minimal packet loss. The simulation results proved that the EEPP-BAP method can improve network performance compared with other models based on different parameters that have been used to construct the controlled movement of MSs in large-scale environments involving urgent messages. The proposed method increased the average lifetime of SNs to 206.6% on average, reduced the average end-to-end delay to 7.1%, and increased the average packet delivery ratio to 36.9%.

The issue “Sink’s Neighbor Problem” hinders the applications of Wireless Sensor Networks (WSNs). Owing to the heavier traffic burden they bear, the nodes close to the sink tend to dissipate their energy faster than others. They die earlier when their energy is used up. The energy efficiency is vital for WSNs because it is unpractical for the sensor nodes to be recharged. An Energy Efficient sink Deployment Scheme (EEDS) aiming at extending the lifespan for wireless sensor networks is proposed. It alleviates the energy consumption imbalance by adopting a mobile sink and regulating its route. Specifically speaking, a mobile sink moves in the monitor area with a certain rate and lingers at the fixed stops. During its move, the sensor nodes collect the data and store them in their memorizers. It is proved that the network lifetime is extended to be approximately six times compared with the static sink used only schemes via mathematical analysis. Besides, the scheme is extensible when the monitor area is enlarged. Finally, the energy efficiency of EEDS is evaluated through comparisons with the strategies only using static or mobile sinks. Experiment results show that EEDS has a good energy efficiency and can extend the network lifespan to be approximately 6 times compared with that of static-sink-used-only schemes. Besides, it is prolonged to be 148 percent of that of strategy using the mobile sink such as GMRE.

Traditional agricultural monitoring systems generally use static sinks to collect data that are often accompanied with some problems such as high construction costs, low precision, poor maintainability and so on. In this paper, we design and implement a smart agricultural management platform that can be divided into three layers: perception layer, network layer and application layer. In perception layer, a large number of ZigBee sensor nodes (SNs) are deployed to build ZigBee networks. In network layer, a mobile sink (MS) that consists of a gateway carried by an unmanned aerial vehicle (UAV) is used to gather data from the perception layer and send it to application layer. Besides, we deploy a full-featured web application in application layer to provide various kinds of services for users. The advice module of the web application is designed to provide users with countermeasures that ensure humidity and temperature are within the ideal range for plant growth. The experimental results show that an MS can gather data from a large number of lands in a short time, with excellent scalability and stable operability. These advantages allow the platform to apply very well to certain areas of particular terrain and it also alleviate the “energy hole” problem in the wireless sensor networks that use static sinks.

Wireless sensor networks (WSNs) include small sensor nodes with battery and processing power and limited memory units then improving power consumption is a major design challenge for any sensor network. In this paper, a new algorithm for routing in the wireless sensor network is proposed using the ultra-innovative algorithm for bacterial Foraging and mobile sink, which leads to energy efficiency. In the proposed method, the number of sensor nodes is determined according to two criteria: the amount of energy on the battery surface and the distance to the sink ahead, which leads to the formation of regular clusters in the network. Nodes adopt a multi-step routing scheme within the network to communicate with the sink. also, the mobile sink is used to balance the load and help consume uniform energy throughout the network. The simulation results show better performance of the proposed method in terms of energy consumption by 17.99%, throughput rate by 30.04%, end-to-end delay by 46.04%, signal-to-noise ratio by 32.81%, delivery rate successfully Data to the sink is 0.80 times higher than the AFSRP (Artificial Fish Swarm Routing Protocol).

Recently, several studies have considered the use of mobile sink (MS) for data gathering in wireless sensor networks. As, it can enhance lifetime of the sensor network by distributing load among the sensors. In some delay-critical applications, a mobile sink is allowed to move along a predefined path. However, due to the predefined path and relatively slower speed of mobile sink, data gathering from sensors may delayed. Thus, time-sensitive data collection in a constrained path environment using mobile sink has increased attention in the research community. Our proposed paper focuses on finding a sub-path on a given path in the network such that the mobile sink can collect maximum data from the network within given time. We refer it as maximum data gathering by optimal sub-path finding problem (MDGOSP). We develop a deterministic algorithm to solve it. Furthermore, performance of the proposed algorithm is validated through simulation experiments using MATLAB.

Wireless Sensor Networks (WSNs) have many applications, such as climate monitoring systems, fire detection, smart homes, and smart cities. It is expected that WSNs will be integrated into the Internet of Things (IoT) and participate in various tasks. WSNs play an important role monitoring and reporting environment information and collecting surrounding context. In this paper we consider a WSN deployed for an application such as environment monitoring, and a mobile sink which acts as the gateway between the Internet and the WSN. Data gathering is a challenging problem in WSNs and in the IoT because the information has to be available quickly and effectively without delays and redundancies. In this paper we propose several distributed algorithms for composite event detection and reporting to a mobile sink. Once data is collected by the sink, it can be shared using the IoT infrastructure. We analyze the performance of our algorithms using WSNet simulator, which is specially designed for event-based WSNs. We measure various metrics such as average residual energy, percentage of composite events processed successfully at the sink, and the average number of hops to reach the sink.

Dissemination of gathered data to sink is one of the main aspects in wireless sensor networks. Gathered data can be sent to the sink either directly or through multi-hop path. For an application like continuous monitoring of data, data have to be reported continuously to sink. As a result, node that form a part of static path towards sink, especially nodes near the sink may dissipate energy rapidly resulting in hotspot problem. Recent studies reveal the use of mobile sink as an efficient method for collecting data, as it avoids the hotspot problem that happens with static sink. While using mobile sink, we need to take care in maintaining dynamic paths and delay constraints. In this paper, we have used the concept of both mobile sink and static sink. Additionally we have introduced a timer and distance based approach for determining a better path. Here, we distribute the load among different CHs and use dynamic shorter paths, as opposed to usual fixed longer paths. Extensive simulations are carried out in Castalia-3.2 for evaluation of our approach. Our results show improved energy efficiency and network lifetime as compared to using static sink and mobile sink separately. The proposed system also reduces end to end delay by which applications like continuous monitoring can be enhanced.

Wireless sensor networks (WSN) consist of large number of sensor nodes. These small and low cost sensor nodes are deployed in monitoring region of interest. Since sensors perform multiple functions such as data gathering and data transmission results in energy depletion, these effects the network lifetime. The nodes having tendency to fail due to energy depletion is the critical issue in WSN. Due to wide number of applications of WSN, it is required to enhance the life time of entire network. Proposed method in this paper is aimed to optimize the energy dissipation by modifying the CH (Cluster Head) selection approach in LEACH (Low Energy Adaptive Clustering Hierarchy) algorithm and avoids the energy-hole problem considering mobility to the sink node which helps to prolong the lifetime of entire network.

Data gathering is a typical operation in wireless sensor networks where data flow through a data gathering tree towards a sink node. Plenty of work had been done on constructing energy-efficient data gathering trees at the routing layer. However, data generation in wireless sensor networks could be bursty as it is driven by the events of interest. Without any sleep scheme, the sensor nodes will stay idle for most of the time. Therefore energy-efficient data gathering trees alone are not enough for energy saving. We propose a routing layer data gathering sleep scheme (DGSS) which could be incorporated into existing data gathering tree formation algorithms to save unnecessary energy consumption due to idle listening and meanwhile adapts to bursty traffic condition. We compare DGSS with DMAC, a popular energy efficient MAC protocol for data gathering, through simulations and show that DGSS can perform better than DMAC for bursty traffic at relatively high data rates.

In the first half of the chapter, the authors provide a comprehensive description of two broad categories of data gathering algorithms for wireless sensor networks: the classical energy-unaware algorithms and the modern energy-aware algorithms, as well as presented an exhaustive performance comparison of representative algorithms from both these categories. While the first half of the chapter focuses on static sink (that is located outside on the network boundary), the second half of the chapter explores the use of mobile sinks that gather data by stopping at the vicinity of the sensor nodes. As a first step, the authors investigate the performance of three different strategies to develop sink mobility models for delay and energy-efficient data gathering in static wireless sensor networks. The three strategies differ on the approach to take to determine the next stop for data gathering: randomly choosing a sensor node that is yet to be covered (Random), choose the sensor node that has the maximum number of uncovered neighbor nodes (Max-Density), and choose the sensor node that has the largest value for the product of the maximum number of uncovered neighbor nodes and the residual energy (Max-Density-Energy). Based on the simulation results, the authors recommend incorporating the random node selection-based strategy to be a better strategy for sink mobility models (with minimal deployment overhead) rather than keeping track of the number of uncovered neighbor nodes per node and the residual energy available at the nodes.